Why excessive agency is a critical risk for corporate AI
Integrating generative AI into business processes opens new opportunities but creates significant vulnerabilities. The OWASP 'Top 10 Risk & Mitigations for LLMs and Gen AI Apps 2025' report identifies 'Excessive Agency' as a distinct risk class. This occurs when an AI model is granted the ability to perform actions beyond its intended business scope. Typical examples include an agent attempting to approve a financial transaction exceeding a user's authorization limit or accessing sensitive customer data unnecessary for the specific task.
BPMN as an architectural control skeleton
The problem of 'unsupervised' AI agents arises when they are deployed as standalone services outside of business logic. According to the BPMN 2.0 standard, a process must be predictable and executable. Integrating an AI agent directly into a BPMN schema allows its role to be restricted to a specific executor within a defined flow. If an agent attempts to bypass a mandatory approval step or perform an unauthorized action, the process engine blocks the request. This approach makes AI behavior deterministic.
Data-level security: RLS and RBAC
To prevent sensitive information disclosure, a logical flow alone is insufficient. It is necessary to implement strict access control at the data level. Using Role-Based Access Control (RBAC) and Row-Level Security (RLS) mechanisms at the platform level allows for limiting the agent's data visibility to only those records it requires. For instance, solutions built on the UnityBase platform utilize these mechanisms to isolate data according to the role of the user or agent.
Human-in-the-loop: an architectural safeguard
Managing AI risks, in accordance with the NIST AI RMF 1.0 framework (Govern, Map, Measure, Manage functions), requires human involvement at critical process nodes. A human-in-the-loop architecture should not be optional—it is a fundamental limitation of the attack surface. For financial or legally significant operations, the agent prepares analytics or a draft, while the final decision remains with a human.
Auditing agent actions: from logs to evidence
Every action taken by an agent must be recorded in immutable event logs. Using process mining, these data can be analyzed to identify deviations from the standard process and attempts to bypass rules. This transforms logs into an evidentiary base necessary for compliance requirements.
Architectural readiness criteria for AI agents
- The agent has a clearly defined scope of actions limited by the BPMN process.
- RBAC/RLS is implemented at the platform level to restrict data access.
- Mandatory check-points (human-in-the-loop) are implemented for critical decisions.
- Every agent action is recorded in immutable event logs.
- An automatic blocking mechanism is configured for actions outside of business logic.
FAQ
How to restrict an AI agent's access to confidential data?
Use RLS (Row-Level Security) and RBAC (Role-Based Access Control) mechanisms at the platform level, ensuring access only to data necessary for a specific role.
Can BPMN serve as a safeguard against AI errors?
Yes, BPMN 2.0 defines an executable process model that blocks agent actions that fall outside the defined business flow.
How to implement AI without violating security policies?
Follow an orchestration-first architecture by implementing human-in-the-loop for critical decisions and ensuring full auditability of actions via event logs.